Semantic and Geometric-Aware Day-to-Night Image Translation Network

Abstract

Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in training deep-learning-based perception models for environments with adverse conditions (e.g., rainy and nighttime). In this paper, we propose an unsupervised network designed for the translation of images from day-to-night to solve the ill-posed problem of learning the mapping between domains with unpaired data. The proposed method involves extracting both semantic and geometric information from input images in the form of attention maps. We assume that the multi-task network can extract semantic and geometric information during the estimation of semantic segmentation and depth maps, respectively. The image-to-image translation network integrates the two distinct types of extracted information, employing them as spatial attention maps. We compare our method with related works both qualitatively and quantitatively. The proposed method shows both qualitative and qualitative improvements in visual presentation over related work.

An Adaptive Semantic Segmentation Network for Adversarial Learning Domain Based on Low-Light Enhancement and Decoupled Generation

“…Accordingly, the body feature F body can be generated first, and the edge feature F edge can be obtained by a specific subtraction operation. If we make F body = ρ(F), then F edge = F − F body , as shown in Equation (7).…”

“…Consequently, a sufficient quantity of accurate segmentation instances is the basis for realizing efficient learning of segmentation models. To tackle this, current schemes such as domain adaptation [6,7], synthetic datasets [8], and style transfer [9] are commonly employed. Due to the low brightness and contrast of nighttime images, this paper transfers daytime images to nighttime images by domain adaptation.…”